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Record W2515591632 · doi:10.1061/9780784480120.022

Seismic Deformation Assessment of a Dam Founded on Low Plastic Fine-Grained Soils under Strong Earthquake Shaking

2016· article· en· W2515591632 on OpenAlexafffundabout
Thuraisamy Thavaraj, Garry Stevenson, David Siu

Bibliographic record

VenueGeo-Chicago 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsKlohn Crippen Berger (Canada)BC Hydro (Canada)
FundersBC HydroTemple University
KeywordsGeotechnical engineeringSoil waterGeologyDeformation (meteorology)Shear (geology)Soil sciencePetrology

Abstract

fetched live from OpenAlex

This paper presents the seismic assessment of a central core embankment dam partially founded on relatively weak, low plastic fine-grained soils. Characterization of low plastic fine-grained soils, especially their cyclic and post-cyclic behavior was critical in assessing the seismic performance of the dam. A site investigation and laboratory testing program was undertaken to characterize the low plastic fine-grained soils and their behavior under the cyclic loading. An effective stress based constitutive model UBCSAND, developed at the University of British Columbia, Canada and implemented in the computer program FLAC, was shown to be able to capture the characteristic cyclic behavior observed in the laboratory cyclic direct simple shear tests. Seismic deformation analyses using the model showed that, although the low plastic fine grained soils will not behave like loose sand, it can still cause significant displacements due to accumulation of strains during cyclic loading.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.224
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes3
Has abstractyes

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